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| 1 | +"""Ejemplo de orquestación MagenticOne con el setup de OpenAIChatClient de este repo. |
| 2 | +
|
| 3 | +Este ejemplo muestra cómo un manager Magentic coordina tres especialistas para |
| 4 | +crear un plan de viaje, con salida en streaming y eventos del ledger de orquestación. |
| 5 | +
|
| 6 | +Run: |
| 7 | + uv run examples/spanish/workflow_magenticone.py |
| 8 | +""" |
| 9 | + |
| 10 | +import asyncio |
| 11 | +import json |
| 12 | +import os |
| 13 | +from typing import cast |
| 14 | + |
| 15 | +from agent_framework import Agent, AgentResponseUpdate, Message, WorkflowEvent |
| 16 | +from agent_framework.openai import OpenAIChatClient |
| 17 | +from agent_framework.orchestrations import MagenticBuilder, MagenticProgressLedger |
| 18 | +from azure.identity.aio import DefaultAzureCredential, get_bearer_token_provider |
| 19 | +from dotenv import load_dotenv |
| 20 | +from rich.console import Console |
| 21 | +from rich.markdown import Markdown |
| 22 | +from rich.panel import Panel |
| 23 | + |
| 24 | +# Configura el cliente OpenAI según el entorno |
| 25 | +load_dotenv(override=True) |
| 26 | +API_HOST = os.getenv("API_HOST", "github") |
| 27 | + |
| 28 | +async_credential = None |
| 29 | +if API_HOST == "azure": |
| 30 | + async_credential = DefaultAzureCredential() |
| 31 | + token_provider = get_bearer_token_provider(async_credential, "https://cognitiveservices.azure.com/.default") |
| 32 | + client = OpenAIChatClient( |
| 33 | + base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/", |
| 34 | + api_key=token_provider, |
| 35 | + model_id=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"], |
| 36 | + ) |
| 37 | +elif API_HOST == "github": |
| 38 | + client = OpenAIChatClient( |
| 39 | + base_url="https://models.github.ai/inference", |
| 40 | + api_key=os.environ["GITHUB_TOKEN"], |
| 41 | + model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"), |
| 42 | + ) |
| 43 | +else: |
| 44 | + client = OpenAIChatClient( |
| 45 | + api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini") |
| 46 | + ) |
| 47 | + |
| 48 | +console = Console() |
| 49 | + |
| 50 | + |
| 51 | +local_agent = Agent( |
| 52 | + client=client, |
| 53 | + instructions=( |
| 54 | + "Sugieres actividades locales auténticas e interesantes o lugares para visitar, " |
| 55 | + "usando cualquier contexto que provea la persona usuaria u otros agentes." |
| 56 | + ), |
| 57 | + name="local_agent", |
| 58 | + description="Specialist in local activities and places.", |
| 59 | +) |
| 60 | + |
| 61 | +language_agent = Agent( |
| 62 | + client=client, |
| 63 | + instructions=( |
| 64 | + "Revisas planes de viaje y das recomendaciones prácticas para retos de idioma " |
| 65 | + "y comunicación en el destino. Si ya está bien cubierto, menciónalo con una razón clara." |
| 66 | + ), |
| 67 | + name="language_agent", |
| 68 | + description="Specialist in language and communication advice.", |
| 69 | +) |
| 70 | + |
| 71 | +travel_summary_agent = Agent( |
| 72 | + client=client, |
| 73 | + instructions=( |
| 74 | + "Sintetizas las sugerencias y recomendaciones de los demás agentes en un plan completo. " |
| 75 | + "Haz suposiciones razonables si faltan detalles. " |
| 76 | + "No hagas preguntas de seguimiento a la persona usuaria. " |
| 77 | + "No pidas confirmaciones ni permisos. " |
| 78 | + "YOUR FINAL RESPONSE MUST BE THE COMPLETE PLAN." |
| 79 | + ), |
| 80 | + name="travel_summary_agent", |
| 81 | + description="Specialist in travel-plan synthesis.", |
| 82 | +) |
| 83 | + |
| 84 | +manager_agent = Agent( |
| 85 | + client=client, |
| 86 | + name="manager_agent", |
| 87 | + description="Magentic manager that coordinates specialists.", |
| 88 | + instructions=( |
| 89 | + "Coordinas especialistas para resolver tareas complejas de forma eficiente. " |
| 90 | + "La persona usuaria no está disponible para preguntas de seguimiento. " |
| 91 | + "Si falta información, elige suposiciones sensatas y continúa. " |
| 92 | + "Asegúrate de terminar con un plan final completo." |
| 93 | + ), |
| 94 | +) |
| 95 | + |
| 96 | +magentic_workflow = MagenticBuilder( |
| 97 | + participants=[local_agent, language_agent, travel_summary_agent], |
| 98 | + manager_agent=manager_agent, |
| 99 | + max_round_count=10, |
| 100 | + max_stall_count=1, |
| 101 | + max_reset_count=1, |
| 102 | +).build() |
| 103 | + |
| 104 | + |
| 105 | +def handle_stream_event(event: WorkflowEvent, last_message_id: str | None) -> str | None: |
| 106 | + """Renderiza un evento del stream y regresa el último message id actualizado.""" |
| 107 | + if event.type == "output" and isinstance(event.data, AgentResponseUpdate): |
| 108 | + message_id = event.data.message_id |
| 109 | + if message_id != last_message_id: |
| 110 | + if last_message_id is not None: |
| 111 | + console.print() |
| 112 | + console.print(f"🤖 {event.executor_id}:", end=" ") |
| 113 | + last_message_id = message_id |
| 114 | + console.print(event.data, end="") |
| 115 | + return last_message_id |
| 116 | + |
| 117 | + if event.type == "magentic_orchestrator": |
| 118 | + console.print() |
| 119 | + emoji = "✅" if event.data.event_type.name == "PROGRESS_LEDGER_UPDATED" else "🧭" |
| 120 | + |
| 121 | + if isinstance(event.data.content, MagenticProgressLedger): |
| 122 | + rendered_content = json.dumps(event.data.content.to_dict(), indent=2) |
| 123 | + console.print( |
| 124 | + Panel( |
| 125 | + rendered_content, |
| 126 | + title=f"{emoji} Orchestrator: {event.data.event_type.name}", |
| 127 | + border_style="bold yellow", |
| 128 | + padding=(1, 2), |
| 129 | + ) |
| 130 | + ) |
| 131 | + elif hasattr(event.data.content, "text"): |
| 132 | + console.print( |
| 133 | + Panel( |
| 134 | + Markdown(event.data.content.text), |
| 135 | + title=f"{emoji} Orchestrator: {event.data.event_type.name}", |
| 136 | + border_style="bold green", |
| 137 | + padding=(1, 2), |
| 138 | + ) |
| 139 | + ) |
| 140 | + else: |
| 141 | + console.print( |
| 142 | + Panel( |
| 143 | + Markdown(str(event.data.content)), |
| 144 | + title=f"{emoji} Orchestrator: {event.data.event_type.name}", |
| 145 | + border_style="bold green", |
| 146 | + padding=(1, 2), |
| 147 | + ) |
| 148 | + ) |
| 149 | + |
| 150 | + return last_message_id |
| 151 | + |
| 152 | + |
| 153 | +def print_final_result(output_event: WorkflowEvent | None) -> None: |
| 154 | + """Imprime el plan final a partir del evento de salida del workflow.""" |
| 155 | + if output_event is None: |
| 156 | + return |
| 157 | + |
| 158 | + output_messages = cast(list[Message], output_event.data) |
| 159 | + console.print( |
| 160 | + Panel( |
| 161 | + Markdown(output_messages[-1].text), |
| 162 | + title="🌍 Plan de viaje final", |
| 163 | + border_style="bold green", |
| 164 | + padding=(1, 2), |
| 165 | + ) |
| 166 | + ) |
| 167 | + |
| 168 | + |
| 169 | +async def main() -> None: |
| 170 | + """Ejecuta el workflow Magentic con salida en streaming.""" |
| 171 | + task = ( |
| 172 | + "Planea un viaje de medio día en Costa Rica para una familia con dos hijos de 6 y 9 años, " |
| 173 | + "hospedada en San José, con presupuesto medio. " |
| 174 | + "Entrega un itinerario completo con horarios, supuestos de transporte, costos estimados, " |
| 175 | + "recomendación de comida y consejos prácticos de idioma." |
| 176 | + ) |
| 177 | + console.print(f"[bold]Tarea:[/bold] {task}\n") |
| 178 | + |
| 179 | + last_message_id: str | None = None |
| 180 | + output_event: WorkflowEvent | None = None |
| 181 | + |
| 182 | + async for event in magentic_workflow.run(task, stream=True): |
| 183 | + last_message_id = handle_stream_event(event, last_message_id) |
| 184 | + if event.type == "output" and not isinstance(event.data, AgentResponseUpdate): |
| 185 | + output_event = event |
| 186 | + |
| 187 | + print_final_result(output_event) |
| 188 | + |
| 189 | + if async_credential: |
| 190 | + await async_credential.close() |
| 191 | + |
| 192 | + |
| 193 | +if __name__ == "__main__": |
| 194 | + asyncio.run(main()) |
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